From 418164bf833d1d7cc4399a2cfe1565a6b576bdb2 Mon Sep 17 00:00:00 2001 From: Ridhi Purohit Date: Sat, 25 Nov 2023 19:51:16 +0000 Subject: [PATCH] added analysis and graphs based on K-Means --- dbscan.ipynb | 1050 +++++++++++++++++++++++++++++--------------------- 1 file changed, 601 insertions(+), 449 deletions(-) diff --git a/dbscan.ipynb b/dbscan.ipynb index 424166f..cca5d4e 100644 --- a/dbscan.ipynb +++ b/dbscan.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "f1d77eb0-187b-4436-b43a-9a4b93d71974", + "metadata": {}, + "source": [ + "# CLUSTERING FOR INFERENCE\n", + "\n", + "This notebook explores unsupervised machine learning to create clusters of cab rides based on geolocation (latitude and longitude). This is done to identify ride trends in Hyde Park before and after UChicago implemented the Lyft Ride program at a cluster level.`" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -14,22 +24,20 @@ " ('spark.eventLog.enabled', 'true'),\n", " ('spark.submit.pyFiles',\n", " '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n", - " ('spark.history.fs.logDirectory',\n", - " 'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/24b513ac-fceb-45d7-87ce-724ecdca7081/spark-job-history'),\n", " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n", " ('spark.sql.autoBroadcastJoinThreshold', '191m'),\n", + " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n", + " 'http://hub-msca-bdp-dphub-students-test-ridhi-m:8088/proxy/application_1700930017790_0001'),\n", " ('spark.kryoserializer.buffer.max', '2000M'),\n", " ('spark.serializer', 'org.apache.spark.serializer.KryoSerializer'),\n", " ('spark.dataproc.sql.local.rank.pushdown.enabled', 'true'),\n", - " ('spark.app.id', 'application_1700888021537_0001'),\n", " ('spark.driver.maxResultSize', '0'),\n", " ('spark.yarn.unmanagedAM.enabled', 'true'),\n", " ('spark.ui.filters',\n", " 'org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter'),\n", - " ('spark.eventLog.dir',\n", - " 'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/24b513ac-fceb-45d7-87ce-724ecdca7081/spark-job-history'),\n", " ('spark.metrics.namespace',\n", " 'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n", + " ('spark.app.id', 'application_1700930017790_0001'),\n", " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n", " 'hub-msca-bdp-dphub-students-test-ridhi-m'),\n", " ('spark.dataproc.sql.optimizer.leftsemijoin.conversion.enabled', 'true'),\n", @@ -37,22 +45,27 @@ " ('spark.executor.id', 'driver'),\n", " ('spark.driver.host',\n", " 'hub-msca-bdp-dphub-students-test-ridhi-m.c.msca-bdp-student-ap.internal'),\n", + " ('spark.eventLog.dir',\n", + " 'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/03233baf-2d0f-4d48-a6a2-cbb1f6d865b2/spark-job-history'),\n", " ('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version', '2'),\n", " ('spark.dynamicAllocation.maxExecutors', '10000'),\n", " ('spark.yarn.dist.pyFiles',\n", " 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'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n", " ('spark.sql.cbo.enabled', 'true'),\n", + " ('spark.driver.port', '36689'),\n", " ('spark.executorEnv.PYTHONPATH',\n", " '/usr/lib/spark/python/lib/py4j-0.10.9-src.zip:/usr/lib/spark/python/:{{PWD}}/pyspark.zip{{PWD}}/py4j-0.10.9-src.zip{{PWD}}/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar{{PWD}}/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar{{PWD}}/com.typesafe_config-1.4.2.jar{{PWD}}/org.rocksdb_rocksdbjni-6.29.5.jar{{PWD}}/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar{{PWD}}/com.github.universal-automata_liblevenshtein-3.0.0.jar{{PWD}}/com.google.cloud_google-cloud-storage-2.16.0.jar{{PWD}}/com.navigamez_greex-1.0.jar{{PWD}}/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar{{PWD}}/it.unimi.dsi_fastutil-7.0.12.jar{{PWD}}/org.projectlombok_lombok-1.16.8.jar{{PWD}}/com.google.guava_guava-31.1-jre.jar{{PWD}}/com.google.guava_failureaccess-1.0.1.jar{{PWD}}/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar{{PWD}}/com.google.errorprone_error_prone_annotations-2.16.jar{{PWD}}/com.google.j2objc_j2objc-annotations-1.3.jar{{PWD}}/com.google.http-client_google-http-client-1.42.3.jar{{PWD}}/io.opencensus_opencensus-contrib-http-util-0.31.1.jar{{PWD}}/com.google.http-client_google-http-client-jackson2-1.42.3.jar{{PWD}}/com.google.http-client_google-http-client-gson-1.42.3.jar{{PWD}}/com.google.api-client_google-api-client-2.1.1.jar{{PWD}}/commons-codec_commons-codec-1.15.jar{{PWD}}/com.google.oauth-client_google-oauth-client-1.34.1.jar{{PWD}}/com.google.http-client_google-http-client-apache-v2-1.42.3.jar{{PWD}}/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar{{PWD}}/com.google.code.gson_gson-2.10.jar{{PWD}}/com.google.cloud_google-cloud-core-2.9.0.jar{{PWD}}/com.google.auto.value_auto-value-annotations-1.10.1.jar{{PWD}}/com.google.cloud_google-cloud-core-http-2.9.0.jar{{PWD}}/com.google.http-client_google-http-client-appengine-1.42.3.jar{{PWD}}/com.google.api_gax-httpjson-0.105.1.jar{{PWD}}/com.google.cloud_google-cloud-core-grpc-2.9.0.jar{{PWD}}/io.grpc_grpc-core-1.51.0.jar{{PWD}}/com.google.api_gax-2.20.1.jar{{PWD}}/com.google.api_gax-grpc-2.20.1.jar{{PWD}}/io.grpc_grpc-alts-1.51.0.jar{{PWD}}/io.grpc_grpc-grpclb-1.51.0.jar{{PWD}}/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar{{PWD}}/io.grpc_grpc-protobuf-1.51.0.jar{{PWD}}/com.google.auth_google-auth-library-credentials-1.13.0.jar{{PWD}}/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar{{PWD}}/com.google.api_api-common-2.2.2.jar{{PWD}}/javax.annotation_javax.annotation-api-1.3.2.jar{{PWD}}/io.opencensus_opencensus-api-0.31.1.jar{{PWD}}/io.grpc_grpc-context-1.51.0.jar{{PWD}}/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar{{PWD}}/com.google.protobuf_protobuf-java-3.21.10.jar{{PWD}}/com.google.protobuf_protobuf-java-util-3.21.10.jar{{PWD}}/com.google.api.grpc_proto-google-common-protos-2.11.0.jar{{PWD}}/org.threeten_threetenbp-1.6.4.jar{{PWD}}/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar{{PWD}}/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar{{PWD}}/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar{{PWD}}/com.fasterxml.jackson.core_jackson-core-2.14.1.jar{{PWD}}/com.google.code.findbugs_jsr305-3.0.2.jar{{PWD}}/io.grpc_grpc-api-1.51.0.jar{{PWD}}/io.grpc_grpc-auth-1.51.0.jar{{PWD}}/io.grpc_grpc-stub-1.51.0.jar{{PWD}}/org.checkerframework_checker-qual-3.28.0.jar{{PWD}}/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar{{PWD}}/io.grpc_grpc-protobuf-lite-1.51.0.jar{{PWD}}/com.google.android_annotations-4.1.1.4.jar{{PWD}}/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar{{PWD}}/io.grpc_grpc-netty-shaded-1.51.0.jar{{PWD}}/io.perfmark_perfmark-api-0.26.0.jar{{PWD}}/io.grpc_grpc-googleapis-1.51.0.jar{{PWD}}/io.grpc_grpc-xds-1.51.0.jar{{PWD}}/io.opencensus_opencensus-proto-0.2.0.jar{{PWD}}/io.grpc_grpc-services-1.51.0.jar{{PWD}}/com.google.re2j_re2j-1.6.jar{{PWD}}/dk.brics.automaton_automaton-1.11-8.jar{{PWD}}/org.slf4j_slf4j-api-1.7.16.jar'),\n", " ('spark.yarn.dist.jars',\n", @@ -70,17 +83,17 @@ " 'com.google.cloud.spark.performance.DataprocMetricsListener'),\n", " ('spark.driver.memory', '8g'),\n", " ('spark.serializer.objectStreamReset', '100'),\n", + " ('spark.ui.proxyBase', '/proxy/application_1700930017790_0001'),\n", " ('spark.executor.memory', '8g'),\n", + " ('spark.app.startTime', '1700930202840'),\n", " ('spark.submit.deployMode', 'client'),\n", " ('spark.executor.cores', '8'),\n", - " ('spark.app.startTime', '1700888229434'),\n", " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n", " ('spark.shuffle.service.enabled', 'true'),\n", " ('spark.scheduler.mode', 'FAIR'),\n", " ('spark.sql.adaptive.enabled', 'true'),\n", " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n", " ('spark.scheduler.minRegisteredResourcesRatio', '0.0'),\n", - " ('spark.driver.port', '37707'),\n", " ('spark.master', 'yarn'),\n", " ('spark.ui.port', '0'),\n", " ('spark.rpc.message.maxSize', '512'),\n", @@ -88,9 +101,6 @@ " ('spark.task.maxFailures', '10'),\n", " ('spark.yarn.isPython', 'true'),\n", " ('spark.dynamicAllocation.enabled', 'true'),\n", - " ('spark.ui.proxyBase', '/proxy/application_1700888021537_0001'),\n", - " ('spark.driver.appUIAddress',\n", - " 'http://hub-msca-bdp-dphub-students-test-ridhi-m.c.msca-bdp-student-ap.internal:34369'),\n", " ('spark.yarn.historyServer.address',\n", " 'hub-msca-bdp-dphub-students-test-ridhi-m:18080'),\n", " ('spark.ui.showConsoleProgress', 'true')]" @@ -130,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "id": "b6c5fdcd-e1af-4e2a-b177-7acc4e3dbec6", "metadata": {}, "outputs": [ @@ -209,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "33165f38-fe04-4100-b615-8419b2d12578", "metadata": {}, "outputs": [], @@ -354,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "410f371d-f9c7-48d5-a795-f1f8ef52eeb0", "metadata": {}, "outputs": [], @@ -373,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "b419e72b-6458-4dd5-942f-0bde079c11e5", "metadata": {}, "outputs": [], @@ -384,7 +394,8 @@ "import matplotlib.cm as cm\n", "from sklearn.cluster import DBSCAN\n", "from sklearn import metrics\n", - "from pyspark.sql.functions import col, radians, acos, sin, cos, lit\n", + "import time\n", + "from pyspark.sql.functions import col, radians, acos, sin, cos, lit, unix_timestamp\n", "import time\n", "from pyspark.ml.feature import VectorAssembler\n", "import geopandas as gpd\n", @@ -397,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "87108e84-9d70-4174-ba1b-b60ce5af2ba0", "metadata": {}, "outputs": [], @@ -408,6 +419,56 @@ "dropAssembler = VectorAssembler(inputCols=[\"dropoff_lat\", \"dropoff_lon\"], outputCol=\"dropoff_features\")" ] }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9e015f7d-4b31-427c-a639-1ac2cd27b6a7", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_trip_duration(df):\n", + " # Convert timestamp columns to Unix timestamps\n", + " df = df.withColumn(\"start_timestamp\", unix_timestamp(\"start_timestamp\"))\n", + " df = df.withColumn(\"end_timestamp\", unix_timestamp(\"end_timestamp\"))\n", + "\n", + " # Calculate trip duration in seconds\n", + " df = df.withColumn(\"trip_duration\", col(\"end_timestamp\") - col(\"start_timestamp\"))\n", + "\n", + " # Convert seconds to minutes\n", + " df = df.withColumn(\"trip_duration\", col(\"trip_duration\") / 60)\n", + "\n", + " return df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "014acbb8-d702-40b0-aa2b-04dd4b50d892", + "metadata": {}, + "outputs": [], + "source": [ + "# creating column to measure trip duration\n", + "\n", + "before = calculate_trip_duration(before)\n", + "after = calculate_trip_duration(after)\n", + "df_2018 = calculate_trip_duration(df_2018)\n", + "df_2019 = calculate_trip_duration(df_2019)\n", + "df_2021 = calculate_trip_duration(df_2021)\n", + "df_2022 = calculate_trip_duration(df_2022)\n", + "df_2023 = calculate_trip_duration(df_2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "4e581e1d-d710-4c5b-87e2-39a5b82c91e8", + "metadata": {}, + "outputs": [], + "source": [ + "before = calculate_trip_duration(before)\n", + "after = calculate_trip_duration(after)" + ] + }, { "cell_type": "markdown", "id": "035a44cd-33b6-4f17-b728-9138787699f0", @@ -418,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "13d8d93a-5637-4871-b9c7-3a44b9ab2a32", "metadata": {}, "outputs": [], @@ -436,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "912fc66b-af5f-400a-bff9-7aab44302897", "metadata": {}, "outputs": [], @@ -449,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "325dfded-9703-4997-828c-c86d4a493e57", "metadata": {}, "outputs": [], @@ -486,38 +547,74 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "id": "f5b59f80-c0d6-4bac-9481-644a39a95f16", "metadata": {}, + "outputs": [], + "source": [ + "def best_k_means(df):\n", + "\n", + " # Fit the model\n", + " cvModel_p = crossval_p.fit(df) \n", + " cvModel_d = crossval_d.fit(df) \n", + "\n", + " # Get the best model from the cross-validation\n", + " bestPickupModel = cvModel_p.bestModel\n", + " bestDropoffModel = cvModel_d.bestModel\n", + "\n", + "\n", + " bestKMeansPickup = bestPickupModel.stages[1] \n", + " bestKMeansDropoff = bestDropoffModel.stages[1] \n", + " \n", + " return bestKMeansPickup, bestKMeansDropoff" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "dfadfe31-66fd-4df6-b574-d3c64e1798c9", + "metadata": {}, + "outputs": [], + "source": [ + "def apply_best_k_means(df, pickAssembler, dropAssembler):\n", + " bestKMeansPickup, bestKMeansDropoff = best_k_means(df)\n", + " \n", + " # Applying the best model on the dataframe\n", + " df = pickAssembler.transform(df)\n", + " df = bestKMeansPickup.transform(df)\n", + "\n", + "\n", + " df = dropAssembler.transform(df)\n", + " df = bestKMeansDropoff.transform(df)\n", + " \n", + " return df\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85a97f41-4929-4d2d-8bc4-a6a2dc2637bd", + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "23/11/25 05:04:38 WARN com.github.fommil.netlib.BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS\n", - "23/11/25 05:04:38 WARN com.github.fommil.netlib.BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS\n", " \r" ] } ], "source": [ - "# Fit the model\n", - "cvModel_p = crossval_p.fit(before) \n", - "cvModel_d = crossval_d.fit(before) \n", - "\n", - "# Get the best model from the cross-validation\n", - "bestPickupModel = cvModel_p.bestModel\n", - "bestDropoffModel = cvModel_d.bestModel\n", + "# Find best k-means models for before and after data\n", "\n", - "\n", - "bestKMeansPickup = bestPickupModel.stages[1] \n", - "bestKMeansDropoff = bestDropoffModel.stages[1] \n" + "before = apply_best_k_means(before, pickAssembler, dropAssembler)\n", + "after = apply_best_k_means(after, pickAssembler, dropAssembler)" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "dfadfe31-66fd-4df6-b574-d3c64e1798c9", + "execution_count": 27, + "id": "9d801fa5-ff78-4d77-aa37-accded102fea", "metadata": {}, "outputs": [ { @@ -549,106 +646,92 @@ " |-- pickup_cluster: integer (nullable = false)\n", " |-- dropoff_features: vector (nullable = true)\n", " |-- dropoff_cluster: integer (nullable = false)\n", + "\n", + "+--------------+\n", + "|pickup_cluster|\n", + "+--------------+\n", + "| 10|\n", + "| 14|\n", + "| 1|\n", + "| 10|\n", + "| 10|\n", + "| 3|\n", + "| 10|\n", + "| 9|\n", + "| 10|\n", + "| 11|\n", + "| 11|\n", + "| 6|\n", + "| 11|\n", + "| 14|\n", + "| 8|\n", + "| 9|\n", + "| 6|\n", + "| 3|\n", + "| 10|\n", + "| 6|\n", + "+--------------+\n", + "only showing top 20 rows\n", "\n" ] } ], "source": [ - "# Applying the best model on the before data\n", - "\n", - "before = pickAssembler.transform(before)\n", - "before = bestKMeansPickup.transform(before)\n", - "\n", - "\n", - "before = dropAssembler.transform(before)\n", - "before = bestKMeansDropoff.transform(before)\n", - "\n", - "before.printSchema()" + "before.printSchema()\n", + "before.select('pickup_cluster').show()" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "d50ddb32-27b2-4a39-a193-f30730c2db94", + "execution_count": null, + "id": "4dd74091-6345-405f-93bd-08f6bafa398e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " \r" + "[Stage 68556:=============> (4 + 12) / 16]\r" ] - } - ], - "source": [ - "# Fit the model for after data\n", - "cvModel_p = crossval_p.fit(after) \n", - "cvModel_d = crossval_d.fit(after) \n", - "\n", - "# Get the best model from the cross-validation\n", - "bestPickupModel = cvModel_p.bestModel\n", - "bestDropoffModel = cvModel_d.bestModel\n", - "\n", - "\n", - "bestKMeansPickup = bestPickupModel.stages[1] \n", - "bestKMeansDropoff = bestDropoffModel.stages[1] " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "afb8a6e4-cb35-4f33-903c-d56ed9fdceb2", - "metadata": {}, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - "root\n", - " |-- ID: string (nullable = true)\n", - " |-- start_timestamp: timestamp (nullable = true)\n", - " |-- end_timestamp: timestamp (nullable = true)\n", - " |-- seconds: integer (nullable = true)\n", - " |-- miles: double (nullable = true)\n", - " |-- pickup_tract: long (nullable = true)\n", - " |-- dropoff_tract: long (nullable = true)\n", - " |-- pickup_area: integer (nullable = true)\n", - " |-- dropoff_area: integer (nullable = true)\n", - " |-- Fare: double (nullable = true)\n", - " |-- Tip: integer (nullable = true)\n", - " |-- total: double (nullable = true)\n", - " |-- pickup_lat: double (nullable = true)\n", - " |-- pickup_lon: double (nullable = true)\n", - " |-- dropoff_lat: double (nullable = true)\n", - " |-- dropoff_lon: double (nullable = true)\n", - " |-- month: integer (nullable = true)\n", - " |-- day_of_month: integer (nullable = true)\n", - " |-- hour: integer (nullable = true)\n", - " |-- day: integer (nullable = true)\n", - " |-- pickup_features: vector (nullable = true)\n", - " |-- pickup_cluster: integer (nullable = false)\n", - " |-- dropoff_features: vector (nullable = true)\n", - " |-- dropoff_cluster: integer (nullable = false)\n", - "\n" + "Total execution time: 2229.8048095703125 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" ] } ], "source": [ - "# Applying the best model on the after data\n", + "# find yearly best clusters of pickup and dropoff locations\n", + "\n", + "start_time = time.time()\n", "\n", - "after = pickAssembler.transform(after)\n", - "after = bestKMeansPickup.transform(after)\n", + "# Apply best k-means for each year\n", + "df_2018 = apply_best_k_means(df_2018, pickAssembler, dropAssembler)\n", + "df_2019 = apply_best_k_means(df_2019, pickAssembler, dropAssembler)\n", + "df_2021 = apply_best_k_means(df_2021, pickAssembler, dropAssembler)\n", + "df_2022 = apply_best_k_means(df_2022, pickAssembler, dropAssembler)\n", + "df_2023 = apply_best_k_means(df_2023, pickAssembler, dropAssembler)\n", "\n", + "end_time = time.time()\n", "\n", - "after = dropAssembler.transform(after)\n", - "after = bestKMeansDropoff.transform(after)\n", + "elapsed_time = end_time - start_time\n", "\n", - "after.printSchema()" + "# Print the elapsed time\n", + "print(f\"Total execution time: {elapsed_time} seconds\")" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 29, "id": "14783651-c521-4cd9-80c6-a28aff637b9d", "metadata": {}, "outputs": [ @@ -686,41 +769,13 @@ } ], "source": [ - "after.printSchema()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "36ad1d50-2b2f-4853-99d0-f1dbef25ad7e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------+---------------+\n", - "|pickup_cluster|dropoff_cluster|\n", - "+--------------+---------------+\n", - "| 1| 3|\n", - "| 6| 11|\n", - "| 5| 13|\n", - "| 5| 12|\n", - "| 5| 1|\n", - "+--------------+---------------+\n", - "only showing top 5 rows\n", - "\n" - ] - } - ], - "source": [ - "after.select('pickup_cluster', 'dropoff_cluster').show(5)" + "df_2018.printSchema()" ] }, { "cell_type": "code", - "execution_count": 21, - "id": "dcb98e61-f37f-4cab-ae9e-547c68899ad4", + "execution_count": 30, + "id": "94e421c0-78b4-4780-aaae-e0deacc8c21d", "metadata": {}, "outputs": [ { @@ -732,52 +787,23 @@ } ], "source": [ - "before_pd = before.select('pickup_lon','pickup_lat', 'pickup_cluster').toPandas()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "cbb0387d-9e6f-4ecc-bf42-7ec5a5cb2f9d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([10, 14, 1, 3, 9, 11, 6, 8, 13, 12, 2, 5, 4, 0, 7],\n", - " dtype=int32)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "before_pd['pickup_cluster'].unique()" + "before_pd = before.select('pickup_lat', 'pickup_lon', 'pickup_cluster','dropoff_lon','dropoff_lat', \\\n", + " 'dropoff_cluster').toPandas()\n", + "after_pd = after.select('pickup_lat', 'pickup_lon', 'pickup_cluster','dropoff_lon','dropoff_lat', \\\n", + " 'dropoff_cluster').toPandas()" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "ba09b6be-72c4-42b7-bc6c-50e887008a31", + "execution_count": 37, + "id": "79aa4cb5-1eb3-483d-8c51-cc170ac15b5e", "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -788,62 +814,88 @@ "import geopandas as gpd\n", "from shapely.geometry import Point\n", "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(16, 12))\n", + "\n", + "# Plot pickup clusters for before_pd\n", + "sns.scatterplot(x=\"pickup_lon\", y=\"pickup_lat\", data=before_pd, hue='pickup_cluster', s=100, alpha=0.7, palette='viridis', ax=axes[0, 0])\n", + "axes[0, 0].set_title('Before Pickup Clusters')\n", + "\n", + "# Plot dropoff clusters for before_pd\n", + "sns.scatterplot(x=\"dropoff_lon\", y=\"dropoff_lat\", data=before_pd, hue='dropoff_cluster', s=100, alpha=0.7, palette='viridis', ax=axes[0, 1])\n", + "axes[0, 1].set_title('Before Dropoff Clusters')\n", "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.lmplot(x=\"pickup_lat\", y=\"pickup_lon\",data = before_pd[before_pd['pickup_lat']!=0.0],fit_reg=False,hue='pickup_cluster',height=10,scatter_kws={\"s\":100})\n" + "# Plot pickup clusters for after_pd\n", + "sns.scatterplot(x=\"pickup_lon\", y=\"pickup_lat\", data=after_pd, hue='pickup_cluster', s=100, alpha=0.7, palette='viridis', ax=axes[1, 0])\n", + "axes[1, 0].set_title('After Pickup Clusters')\n", + "\n", + "# Plot dropoff clusters for after_pd\n", + "sns.scatterplot(x=\"dropoff_lon\", y=\"dropoff_lat\", data=after_pd, hue='dropoff_cluster', s=100, alpha=0.7, palette='viridis', ax=axes[1, 1])\n", + "axes[1, 1].set_title('After Dropoff Clusters')\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 24, - "id": "91b07045-a3b7-45a1-8d59-1a5ea3044dcb", + "execution_count": 81, + "id": "dbde458f-1123-4196-ae9f-4ff9eb508170", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "text/plain": [ - "array([ 1, 6, 5, 11, 14, 4, 12, 3, 9, 2, 10, 13, 8, 0, 7],\n", - " dtype=int32)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "after_pd = after.select('pickup_lon','pickup_lat', 'pickup_cluster').toPandas()\n", - "after_pd['pickup_cluster'].unique()" + "from geopy.distance import great_circle\n", + "from shapely.geometry import Point, MultiPoint\n", + "\n", + "\n", + "# Define the get_centermost_point function\n", + "def get_centermost_point(cluster):\n", + " centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)\n", + " centermost_point = min(cluster, key=lambda point: great_circle(point, centroid).m)\n", + " return tuple(centermost_point)\n", + "\n", + "before_pickup_centers = before_pd.groupby('pickup_cluster')\\\n", + " .apply(lambda cluster: get_centermost_point(list(zip(cluster['pickup_lon'], cluster['pickup_lat']))))\\\n", + " .reset_index(level=0)\\\n", + " .rename(columns={'pickup_cluster': 'center_pickup_cluster', 0: 'center_coordinates'})\n", + "\n", + "before_pickup_centers[['center_lon', 'center_lat']] = before_pickup_centers['center_coordinates'].apply(pd.Series)\n", + "\n", + "after_pickup_centers = after_pd.groupby('pickup_cluster')\\\n", + " .apply(lambda cluster: get_centermost_point(list(zip(cluster['pickup_lon'], cluster['pickup_lat']))))\\\n", + " .reset_index(level=0)\\\n", + " .rename(columns={'pickup_cluster': 'center_pickup_cluster', 0: 'center_coordinates'})\n", + "\n", + "after_pickup_centers[['center_lon', 'center_lat']] = after_pickup_centers['center_coordinates'].apply(pd.Series)\n", + "\n", + "before_dropoff_centers = before_pd.groupby('dropoff_cluster')\\\n", + " .apply(lambda cluster: get_centermost_point(list(zip(cluster['dropoff_lon'], cluster['dropoff_lat']))))\\\n", + " .reset_index(level=0)\\\n", + " .rename(columns={'dropoff_cluster': 'center_dropoff_cluster', 0: 'center_coordinates'})\n", + "\n", + "before_dropoff_centers[['center_lon', 'center_lat']] = before_dropoff_centers['center_coordinates'].apply(pd.Series)\n", + "\n", + "after_dropoff_centers = after_pd.groupby('dropoff_cluster')\\\n", + " .apply(lambda cluster: get_centermost_point(list(zip(cluster['dropoff_lon'], cluster['dropoff_lat']))))\\\n", + " .reset_index(level=0)\\\n", + " .rename(columns={'dropoff_cluster': 'center_dropoff_cluster', 0: 'center_coordinates'})\n", + "\n", + "after_dropoff_centers[['center_lon', 'center_lat']] = after_dropoff_centers['center_coordinates'].apply(pd.Series)\n" ] }, { "cell_type": "code", - "execution_count": 25, - "id": "b354b23f-c152-4af6-9059-fc27bbe30a4c", + "execution_count": 83, + "id": "cd6656bf-8093-4428-8402-48e08f1258fc", "metadata": {}, "outputs": [ { "data": { + "image/png": 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- "text/plain": [ - "
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" ] }, "metadata": {}, @@ -851,346 +903,446 @@ } ], "source": [ - "sns.set_style(\"whitegrid\")\n", - "sns.lmplot(x=\"pickup_lat\", y=\"pickup_lon\",data = after_pd[after_pd['pickup_lat']!=0.0],fit_reg=False,hue='pickup_cluster',height=10,scatter_kws={\"s\":100})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "dbde458f-1123-4196-ae9f-4ff9eb508170", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " center_lon center_lat\n", - "0 -87.587479 41.805912\n", - "1 -87.601285 41.790469\n", - "2 -87.598697 41.783225\n", - "3 -87.589607 41.797965\n", - "4 -87.596183 41.808916\n", - "5 -87.594925 41.778877\n", - "6 -87.582630 41.783034\n", - "7 -87.604209 41.813201\n", - "8 -87.594015 41.790567\n", - "9 -87.603746 41.797827\n", - "10 -87.592311 41.794090\n", - "11 -87.585303 41.801227\n", - "12 -87.603559 41.783171\n", - "13 -87.590431 41.790448\n", - "14 -87.598945 41.797971\n", - " center_lon center_lat\n", - "0 -87.587479 41.805912\n", - "1 -87.601285 41.790469\n", - "2 -87.598697 41.783225\n", - "3 -87.589607 41.797965\n", - "4 -87.596183 41.808916\n", - "5 -87.594925 41.778877\n", - "6 -87.582630 41.783034\n", - "7 -87.604209 41.813201\n", - "8 -87.594015 41.790567\n", - "9 -87.603746 41.797827\n", - "10 -87.592311 41.794090\n", - "11 -87.585303 41.801227\n", - "12 -87.603559 41.783171\n", - "13 -87.590431 41.790448\n", - "14 -87.598945 41.797971\n" - ] - } - ], - "source": [ - "from geopy.distance import great_circle\n", - "from shapely.geometry import Point, MultiPoint\n", - "import pandas as pd\n", "\n", + "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(16, 12))\n", "\n", - "# Define the get_centermost_point function\n", - "def get_centermost_point(cluster):\n", - " centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)\n", - " centermost_point = min(cluster, key=lambda point: great_circle(point, centroid).m)\n", - " return tuple(centermost_point)\n", - "\n", - "before_pickup_centers = before_pd.groupby('pickup_cluster')\\\n", - " .apply(lambda cluster: get_centermost_point(list(zip(cluster['pickup_lon'], cluster['pickup_lat']))))\n", - "\n", - "before_pickup_centers = pd.DataFrame(before_pickup_centers.tolist(), columns=['center_lon', 'center_lat'])\n", + "# Plot center pickup clusters for before_pd\n", + "sns.scatterplot(x=\"center_lon\", y=\"center_lat\", data=before_pickup_centers, hue='center_pickup_cluster', s=100, alpha=0.7, palette='viridis', ax=axes[0, 0])\n", + "axes[0, 0].set_title('Before Pickup Center Clusters')\n", "\n", + "# Plot center dropoff clusters for before_pd\n", + "sns.scatterplot(x=\"center_lon\", y=\"center_lat\", data=before_dropoff_centers, hue='center_dropoff_cluster', s=100, alpha=0.7, palette='viridis', ax=axes[0, 1])\n", + "axes[0, 1].set_title('Before Dropoff Center Clusters')\n", "\n", - "after_pickup_centers = before_pd.groupby('pickup_cluster')\\\n", - " .apply(lambda cluster: get_centermost_point(list(zip(cluster['pickup_lon'], cluster['pickup_lat']))))\n", + "# Plot center pickup clusters for after_pd\n", + "sns.scatterplot(x=\"center_lon\", y=\"center_lat\", data=after_pickup_centers, hue='center_pickup_cluster', s=100, alpha=0.7, palette='viridis', ax=axes[1, 0])\n", + "axes[1, 0].set_title('After Pickup Center Clusters')\n", "\n", - "after_pickup_centers = pd.DataFrame(after_pickup_centers.tolist(), columns=['center_lon', 'center_lat'])\n", + "# Plot center dropoff clusters for after_pd\n", + "sns.scatterplot(x=\"center_lon\", y=\"center_lat\", data=after_dropoff_centers, hue='center_dropoff_cluster', s=100, alpha=0.7, palette='viridis', ax=axes[1, 1])\n", + "axes[1, 1].set_title('After Dropoff Center Clusters')\n", "\n", + "plt.tight_layout()\n", "\n", - "print(before_pickup_centers)\n", - "print(after_pickup_centers)\n" + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 35, - "id": "de26371d-101f-418b-abc8-70f32b371ac0", + "execution_count": 97, + "id": "3685f0c6-5ac2-464f-a5e4-c0b05d30d5a1", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "before_drop = before.select('dropoff_lon','dropoff_lat', 'dropoff_cluster').toPandas()\n", - "after_drop = after.select('dropoff_lon','dropoff_lat', 'dropoff_cluster').toPandas()" + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "# Plot pickup center points for before_pd\n", + "sns.scatterplot(x=\"center_lon\", y=\"center_lat\", data=before_pickup_centers, hue='center_pickup_cluster', s=100, alpha=0.7, palette='Reds', marker=\"x\", linewidth=2, label='Before')\n", + "\n", + "# Plot pickup center points for after_pd\n", + "sns.scatterplot(x=\"center_lon\", y=\"center_lat\", data=after_pickup_centers, hue='center_pickup_cluster', s=100, alpha=0.7, palette='Greens', marker=\"o\", edgecolor='darkgreen',label='After')\n", + "\n", + "# Set labels and legend\n", + "plt.xlabel('Longitude')\n", + "plt.ylabel('Latitude')\n", + "plt.title('Before & After - Pickup Center Points')\n", + "plt.legend(bbox_to_anchor=(1, 1), loc='upper left')\n", + "\n", + "# Show the plot\n", + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 37, - "id": "3685f0c6-5ac2-464f-a5e4-c0b05d30d5a1", + "execution_count": 98, + "id": "960d215b-b04c-448e-a779-5670abd47192", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " center_lon center_lat\n", - "0 -87.589607 41.797965\n", - "1 -87.582366 41.797153\n", - "2 -87.603746 41.797827\n", - "3 -87.594925 41.778877\n", - "4 -87.596183 41.808916\n", - "5 -87.601285 41.790469\n", - "6 -87.587479 41.805912\n", - "7 -87.608427 41.783101\n", - "8 -87.598945 41.797971\n", - "9 -87.594015 41.790567\n", - "10 -87.582630 41.783034\n", - "11 -87.592311 41.794090\n", - "12 -87.590431 41.790448\n", - "13 -87.585303 41.801227\n", - "14 -87.598697 41.783225\n", - " center_lon center_lat\n", - "0 -87.587479 41.805912\n", - "1 -87.601285 41.790469\n", - "2 -87.594925 41.778877\n", - "3 -87.592311 41.794090\n", - "4 -87.603746 41.797827\n", - "5 -87.583144 41.790506\n", - "6 -87.598697 41.783225\n", - "7 -87.579948 41.776164\n", - "8 -87.582366 41.797153\n", - "9 -87.594015 41.790567\n", - "10 -87.585303 41.801227\n", - "11 -87.594266 41.801671\n", - "12 -87.589607 41.797965\n", - "13 -87.590431 41.790448\n", - "14 -87.596183 41.808916\n" - ] + "data": { + "image/png": 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JKxedi1OXID+fZZ5N09RXX32loKCgs37fSq8pPT29zHHS0tJksVjKLLmcnp7u9D0oLi7W8ePHHR/qS/+blpamGjVqlDlm6TldoWXLloqIiNAXX3yhe+6556zz1Jzv++RcuHO8lKpbt26Z73Gpqvw+nMnll1+uyy+/XDabTRs3btRbb72lp59+WlFRUerfv7/bzw8AQEUQ1AAAXGLr1q2S/vww2KNHDy1fvlx2u/2MHwrL07lzZ73++utaunSp/v73vzvaP/30U+Xm5paZ9PR0evToof/973+qV6+ewsPDz+n8TZo0kVSypPYVV1zhaL/ttttkt9v14osvKiQkRBMnTiz3ODt37tS6devUt29f3XzzzWW2z549W1988YWOHTt2xg+qpZMm5+fnO7W3bdtWYWFh2rFjh0uXNpd0xg/blTFz5kzt2LFDY8aMkb+/f7l9GzZsqNjYWH300UcaNWqUI+jIzc3VypUr1aZNmzKTvi5btszpjpSPP/5YxcXF6tChgySpU6dOkqSlS5cqISHB0W/9+vXauXOnxowZ45LrlCRfX1/deuutev755/Xyyy+XWZ5bKrmLZs+ePWrXrt15v09OxxPjpSKq6vtQev3l3WVjtVrVunVrNWrUSMuWLdOmTZsIagAAXoOgBgBwzrZv3y6bzSap5HGGlStXatWqVbrqqqtUt25dSVL//v21bNky3XbbbRo+fLgSEhLk6+urQ4cO6ccff1Tv3r3LXe73iiuuUFJSkp5//nllZ2erbdu22rZtm/7973+refPmuu66685a55133qnVq1dr2LBhGj58uBo2bKjCwkKlpKTom2++0eOPP17mN/ul4uLi9Ne//lXvvvuusrOzNWjQIIWGhmr79u16//33VaNGDR0+fFjTp0/XQw89dMYaSu9MufXWW50+nJbKycnR999/r6VLl57x8YvS0Oi9995TcHCw/P39VadOHUVGRuqhhx7SAw88oMzMTPXt21fVq1fX0aNHtXXrVh09elSPP/74Wb9OrnLixAn9+uuvkqS8vDzt2rVLK1as0M8//6yrr75a48ePP+sxLBaL7rvvPt177736xz/+oRtvvFGFhYWaO3euTpw4oXvuuafMPp999pmsVquuuOIKbd++XdOnT1fTpk119dVXS5IaNWqkG2+8UW+//bYsFovjUb3p06erZs2aTkGgK9x6663atWuXZsyYoQ0bNmjAgAGqWbOmsrKytGbNGr333nsaP3682rVrd97vk9Px1vFSVd+HuLg4SdJ//vMfDR48WD4+PmrYsKGWLVumH374QT169FDNmjVVUFDguNutS5cuLjk3AACuQFADADhnkyZNcvw9NDRUderU0aRJk3TTTTc52q1Wq2bPnq358+frww8/1Kuvviqr1aoaNWqoffv2jg9TZ2IYhmbNmqUZM2Zo8eLFeuWVVxQREaHrrrtOd999t9PS3GcSExOj5ORkzZo1S3PnztXhw4cVHBys2rVrq2vXrmUeoTnVo48+qpYtW2rhwoWaOHGiTNNUw4YNdeONN2r48OH673//q2eeeUZWq9Xpa1KqqKhIH374oZo1a3bakEaSunfvrho1aig5OfmMQU3dunU1efJkzZ8/XyNGjJDNZnPMGXPdddepVq1aev311/Xoo48qJydH1apVU7NmzcpM2upua9eu1Y033ijDMBQYGKjY2FglJCRo7NixZ53H52TXXnutAgMD9eqrr2rChAmOux/mz5+vtm3bluk/Y8YMzZgxQ++++64Mw1CvXr00efJkpzHy2GOPqW7dukpOTtY777yjkJAQde3aVffcc4/LH7kxDENTp07VlVdeqffee09PP/20Tpw4oeDgYDVt2lT33nuvY76f832fnI43j5eq+D507NhR//jHP/TBBx/o/fffl91u1/z589WsWTOtWrVKM2bMUHp6uoKCghQXF6fZs2ef0/gEAMDdDPN8l3EAAADwgBkzZmjmzJn6/vvvy0w6DQAAcKFieW4AAAAAAAAvQVADAAAAAADgJXj0CQAAAAAAwEtwRw0AAAAAAICXIKgBAAAAAADwEgQ1AAAAAAAAXsLH0wVczOx2u4qLi2WxWGQYhqfLAQAAAABc5EzTlN1ul4+PjywW7s24EBHUuFFxcbE2bNjg6TIAAAAAAJeYVq1ayc/Pz9NloBIIatyoNL1s1aqVrFarh6vBhcZms2nDhg2MH7gE4wmuxpiCKzGe4GqMKbjShTaeSuvlbpoLF0GNG5U+7mS1Wi+INzS8E+MHrsR4gqsxpuBKjCe4GmMKrnShjSem37hwEbEBAAAAAAB4CYIaAAAAAAAAL+E1Qc2cOXMUHx+vKVOmONpWrlypUaNGqWPHjoqPj9eWLVsqdKw333xTffv2VUJCgrp3766nn35aBQUFju1r1qzRmDFjlJSUpPj4eH3++edljmGapmbMmKGkpCQlJCRo+PDh2r59+/lfKAAAAAAAwBl4xRw169ev18KFCxUfH+/Unpubq8TERPXr108PPfRQhY61dOlSvfDCC3r66aeVmJioPXv26IEHHpAkTZ482XHc+Ph4DRkyROPHjz/tcV577TXNmzdPzzzzjBo0aKDZs2dr5MiR+uSTTxQSEnIeVwsAAAAAgOeYpqni4mLZbDZPl3LJsFqt8vHxqdDcQR4PanJycnTffffpqaee0uzZs522DRo0SJKUkpJS4eP9+uuvatu2ra699lpJUp06dTRgwACtX7/e0ad79+7q3r37GY9hmqbmz5+vMWPGqE+fPpKkadOmqUuXLvroo480bNiwCtcDAAAAAIC3KCwsVGpqqnJzcz1dyiUnKChINWvWPOuy6R4Pap544gl1795dXbp0KRPUVEa7du20dOlSrV+/XgkJCdq/f7/+97//afDgwRU+RkpKitLT05WUlORo8/PzU/v27bVu3bpzDmpIKVEZpeOG8QNXYDzB1RhTcCXGE1yNMQVXutDGU3l12u127d69W1arVbVq1ZKfnx+rQ1UB0zRVWFio9PR07d69W02aNCl3+XSPBjXLly/X5s2blZyc7LJj9u/fX0ePHtVNN93kuJ3rr3/9q2677bYKHyM9PV2SVL16daf2qKgoHTx48Jxr2rBhwznvA5Ri/MCVGE9wNcYUXInxBFdjTMGVLobxVFhYKLvdrrp16yooKMjT5VxSAgMD5evrq71796qwsFABAQFn7OuxoCY1NVVTpkzRG2+8IX9/f5cd98cff9Qrr7yiRx99VAkJCdq3b5+mTJmil19+WXfcccc5HevUZNE0zUrV1KpVK1mt1krti0uXzWbThg0bGD9wCcYTXI0xBVdiPMHVGFNwpQttPJXWW57y7uaA+1T06+6xoGbTpk3KyMjQkCFDHG02m01r1qzRggULtGHDhkq9CaZPn66BAwdq6NChkqT4+Hjl5ubqkUce0dixYyv0hYmOjpYkHTlyRDExMY72jIwMRUVFnXNNVqv1gnhDwzsxfuBKjCe4GmMKrsR4gqsxpuBKl/R4Mu2ScQ7hzrn2hxOPBTWdOnXSsmXLnNomTZqkRo0aafTo0ZV+A+Tn55cJY6xWq0zTrPAdMXXq1FF0dLRWrVql5s2bSyq5RWzNmjW69957K1UXAAAAAAAXHFuRVFwo+fhL1gpECMWFJfv4BkiWSzTYOk8eC2pCQkIUFxfn1BYUFKSIiAhH+/Hjx5Wamqq0tDRJ0u7duyWVzBVTetfLxIkTFRsbq3vuuUeS1LNnT82bN0/Nmzd3PPo0ffp09erVyxH+5OTkaN++fY7zpqSkaMuWLQoPD1etWrVkGIZGjBihOXPmqEGDBqpfv77mzJmjgIAADRgwwL1fGAAAAAAAvIHdJhUVlvy9uKDkv+WFNcWFUnFRyd+L8iW/QK+7s2bGjBl69913lZGRoZdffllXXnmlp0sqw+OrPpXnyy+/1KRJkxyvJ0yYIEkaN26cxo8fL6lkrpuT76AZO3asDMPQSy+9pMOHD6tatWrq2bOnY19J2rhxo0aMGOF4PXXqVEnS4MGD9cwzz0iSRo8erYKCAj3++OPKzMxU69at9cYbbygkJMR9FwwAAAAAgLcwLCXBjK1YMlV+WHNySCNJVj+XhjQPPPCAPvjgA8friIgItWzZUvfdd5+aNm1aoWPs3LlTM2fO1Msvv6zWrVsrPDzcZfW5klcFNW+99ZbT6yFDhjjNYVORfXx8fDRu3DiNGzfujPt07NhR27ZtK/e4hmFo/PjxjkAIAAAAAIBLimFIPn4lfy8vrDk1pPHxk3x8XV5O165dHTdaHDlyRC+99JLGjBmjr7/+ukL7lz5Z07t37/NalryoqEi+vq6/vlLedQ8SAAAAAADwHqVhTWkwUxrW2IpLXldRSCNJfn5+io6OVnR0tJo1a6bRo0crNTVVR48elSQdPnxYd911l9q3b6+OHTtq7NixSklJkVTyyNOYMWMkSU2bNlV8fLwkyW63a+bMmerWrZtatmyp6667Tt98843jnCkpKYqPj9eKFSs0fPhwtWrVSkuXLpUkLVq0SFdffbVatWqlfv36acGCBS65ToIaAAAAAABwZmcKawrzqiykOVVOTo6WLl2q+vXrKyIiQnl5eRoxYoSCgoL09ttv65133lFQUJBuvfVWFRYW6v/+7/8cd+N89913+u677yRJ8+fP17x583T//fdr6dKlSkpK0u233649e/Y4ne/555/X8OHDtWLFCiUlJem9997Tiy++qAkTJmjFihW6++679e9//9vp8azK8qpHnwAAAAAAgBc63WNQpv3P7VUQ0nz99ddKTEyUJOXm5io6Olpz5syRxWLR8uXLZRiGpkyZ4nisaerUqWrfvr1++uknJSUlKSwsTJIcixNJ0ty5czV69Gj1799fknTffffpxx9/1H/+8x89+uijjn633HKL+vTp43g9a9YsPfDAA462unXraseOHVq4cKEGDx58XtdJUAMAgJcxbcWSxVrhZ6dNW7GMiiyXCQAAcD5Kw5rSx55KWSxVcidNx44d9dhjj0mSMjMz9c4772j06NF6//33tWnTJu3bt09t27Z12qegoMBp1eeTZWdnKy0trcw+bdu21datW53aWrZs6fj70aNHlZqaqgcffFAPP/ywo724uFihoaHnc4mSCGoAAPAqZlGhzM3fS9VqSHXiVGQr0qodq/Tdju+UmZcpP6uv6lWrr6tbXa361evLLMgr6V+jgYyajTxdPgAAuNjZisq2mfaS8MbNvzgKDAxU/fr1Ha9btGihyy+/XO+9957sdrtatGih559/vsx+1apVK/e4p/5yzDTNMm1BQUGOv9vtJXcSPfnkk2rdurVTv5NXpa4sghoAALyEabfJ3Py9zNwsmTkn9OHWz/T+7m8V5B+kK5p0UYvgZrLZi7Xt0Hbd+d/xio9uonE1O6i2T7DMPZtlsfrIiKnn6csAAAAXq1MnDi51tqW73cQwDBmGoYKCArVo0UIff/yxqlevrpCQkArtHxISopiYGP3yyy9q3769o33dunVKSEg4435RUVGKjY3V/v37NXDgwPO+jlMR1AAA4CUMi1WKrSf7ro16ZdsnWp21V7f1Gq0Wjdo7/Vane9PuuqnDUC394T3d8/0cPdV6mJrEXCZFRJdzdAAAgPNwutWdrD4l7eUt3e1ChYWFSk9PlySdOHFCb7/9tnJzc9WzZ08lJCRo7ty5Gjt2rP75z38qNjZWqampWrlypW699VbVqFHjtMccNWqUZsyYoXr16qlp06ZavHixtm7deto7c042fvx4PfXUUwoJCVG3bt1UWFiojRs36sSJExo5cuR5XSdBDQAAXsSo0VAfbflMq0/s0UPX3KOo4GpSfo4UeNJvhuw2hRQV6abEgQr1C9Tj6xfpldFvKcwv0HOFAwCAi1d5S3CfOsGwG8Oab7/9VklJSZKk4OBgNWrUSNOnT1fHjh0lSW+//baef/55jRs3Tjk5OYqNjVXnzp3LvcNmxIgRys7O1jPPPKOjR4+qcePGmjVrlho0aFBuLUOHDlVAQIDmzp2r5557TkFBQYqLi9Mtt9xy3tdJUAMAgBcpthXr/Z3/0y29bi0JaaSSoEYqCWvsNinrWMl/JQ1o2Vcbju7TZ9u/1fXtrvdQ1QAA4KJVXkgjnX41KDeENc8884yeeeaZcvtER0dr2rRpZ9x+5ZVXatu2bU5tFotF48aN07hx4067T506dcrsU+raa6/Vtddee5bKz935z3IDAABc5oddP8hqsahN445S0EmrBuTnSDknnEIaWawywqqpd4veWr7hI5mm6ZmiAQDAxcluKz+kKVUa1pQGM6VhzcnLd6PCCGoAAPAiP+z6QZ0v61QyJ41/kHNYU5jnFNIoNFKyWNWuQTsdzz2uPUf2eKRmAABwkTIszo84lbcE96lhjY9fyf44Z3zVAADwIifyTygyOPLPBv8gKSC4bMc/QhpJ8rH6KDQwVFkFWVVUJQAAuCQYhmT1lfwCyg9pTu7v41fS31qB/jgtghoAALyIn9VPBaXPdUsld9AU5pftWJDn9LLIViR/H383VwcAAC45huH45ZBb+qMMghoAALxIg6gG2pb6x4R1p0wc7CQ/R8rLliSlHk9VTkGOaobXrMJKAQAA4A4ENQAAeJG+LfpqQ8omHTmRVmbiYIVHlZ1gOC9bX2z+Ul0v66awwDDPFA0AAACXIagBAMCLRIdGq32dRC367m2ZtuKSxpMmDj51guHUtN36duMXuqbVNR6qGAAAAK5EUAMAgBcxC/I0JiZRv+/+TQt++UCmYXGaOFiSI6xJPZGm5z6bqf4R8Wpqs7I8NwAAwEXAx9MFAACAEqbdJnPLD6pu+Gpq4t/02MZkPXT8gHq36K0uTTorwDdAkrQvY7++2Pylftjyja6NjNeIy3rJTNkuw9dfqtHAsxcBAACA80JQAwCAlzAsVql2E5k7f1WtanU089b5+nbvGi39baneXv2Ogv2DVGQrlt1uU8/4Xnrur/9WY7tV9t2bZASHSdVrefoSAAAAcJ4IagAA8CJGdB1ZrFYpJEIBfoG6qvlVurLZlUrPSldmXqZ8rb6KCY1RkH+QYx+Lj58UHi3D18+DlQMAALjXggULNHfuXKWnp6tJkyaaPHmyLr/8ck+X5XLMUQMAgJcxqtWU4Rf452vDUExYjJrENlGDqAZOIY0kGVG1CWkAAEDVMU3JViwVF5b8twrmyVuxYoWmTp2qsWPHasmSJWrXrp1Gjx6tgwcPuv3cVY2gBgAAAAAAVExxkZSfJRXkSIV5Jf/Nzyppd6N58+bp+uuv19ChQ9W4cWM9+OCDqlGjht599123ntcTCGoAAAAAAMDZFRdJhbll76AxzZJ2N4U1hYWF2rRpk5KSkpzar7jiCq1bt84t5/QkghoAAAAAAFA+05SK8srvU5Tnlsegjh07JpvNpurVqzu1R0VFKT093eXn8zSCGgAAAAAAUD677ewhjGmW9HMTwzBOOZ1Zpu1iQFADAAAAAADKZ9pd2+8cREZGymq16siRI07tGRkZioqKcvn5PI2gBgAAAAAAlM+oYHxQ0X7nwM/PTy1atNCqVauc2levXq3ExESXn8/TfDxdAAAAAAAA8HIWq2QY5T/+ZBgl/dxg5MiRmjhxolq2bKnExEQtXLhQqampGjZsmFvO50kENQAAAAAAoHyGIfkGlqzudCa+gSX93OCaa67RsWPHNGvWLKWlpSkuLk6vvvqqateu7ZbzeRJBDQAAAAAAODsfX0lBZVd3Kg1xfHzdevqbb75ZN998s1vP4Q0IagAAAAAAQMX4+EpWnz9WgbKXzElT+lgUXIKgBgAAAAAAVJxhlIQ1cAtWfQIAAAAAAPASBDUAAAAAAABegqAGAAAAAADASxDUAAAAAAAAeAmCGgAAAAAAAC9BUAMAAAAAAOAlCGoAAAAAAAC8BEENAAAAAACAl/DxdAEAAAAAAODCYZqmlJct2Yokq68UGCLDMNx2vjlz5mjlypXatWuXAgIClJiYqHvvvVeNGjVy2zk9iaAGAAAAAABUiJl9TErfXxLSlLL6yoyuKyMk0i3n/Omnn3TzzTerVatWstlsevHFFzVq1CgtX75cQUFBbjmnJxHUAAAAAACAszKzj0mHdpXdYCuSDu2SWaORW8KauXPnOr2eOnWqOnfurE2bNql9+/YuP5+nMUcNAAAAAAAol2maJXfSlOfI/pJ+bpaVlSVJCg8Pd/u5PIGgBgAAAAAAlK90TpryFBeV9HMj0zQ1depUtWvXTnFxcW49l6fw6BMAAAAAACjf2UKac+1XSU888YR+//13vfPOO249jycR1AAAAAAAgPJZfV3brxKefPJJffnll3r77bdVo0YNt53H0whqAAAAAABA+QJDSkKY8u6Y8SlZqtvVTNPUk08+qc8++0xvvfWW6tat6/JzeBPmqAEAAAAAAOUyDEOKPktAElW3pJ+LPf7441q6dKleeOEFBQcHKz09Xenp6crPz3f5ubwBd9QAAAAAAICzMkIiZdZoVLL608l31vj4loQ0bliaW5LeffddSdLw4cOd2qdOnaohQ4a45ZyeRFADAAAAAAAqxAiJlBkc8ecqUNaSx53ccSdNqW3btrnt2N6IoAYAAAAAAFSYYRhSUKiny7hoMUcNAAAAAACAlyCoAQAAAAAA8BIENQAAAAAAAF6CoAYAAADwMuaxNNk3fC/TNCvWPy1F5tZf3FwVAKAqMJkwAAAA4EXMY2kyv1wss7BAKi6W2iSVu5qKmZYi+/8+lIqLZbHbZTRvX4XVAgBcjTtqAAAAAG9y7EhJSCPJ3LpW5q/fnfHOmpNDGkky0w/KNO1VVioAwPUIagAAAAAvYjRqLkuH3o7XZwprTg1pjFoNZCT1l2HwT3wAuJDxUxwAAADwMkbjluWGNWcMaazMbAAAFzp+kgMAAABeyGjcUhZJ9p++kFQS1kiSajckpAGAixg/zQEAAAAvdbqwxhHYiJAGgGeYdrt0aI/M3CwZQaFSjQYyLO57YGfGjBmaOXOmU1tUVJRWrVrltnN6Ej/RAQAAAC92aljjaK9Zn5AGQJUzd22UffVHUs6JkteSFBwmS5cBMhq1dNt5mzRponnz5jleW61Wt53L0/ipDgAAAHi70IjTt1ku3g8qALyPuWuj7J+9U3ZDzgnZP3tHlqtucltYY7VaFR0d7ZZjexsmEwYAAAC8mGPi4FPbf/+t3KW7AcCVTLu95E6acthXLy95LMoN9u7dq6SkJPXq1UsTJkzQ/v373XIeb0BQAwAAAHip063uZGnX48/tZ1i6GwBc7tAex+NOZ5STWdLPxRISEjRt2jTNnTtXTz31lI4cOaJhw4bp2LFjLj+XN+DRJwAAAMALlbcEt8VqLbsaVJskGYbhqXIBXOTM3KwK93P1T6Lu3bs7vW7Tpo2uuuoqLVmyRCNHjnTx2TyPO2oAAAAAL1NeSCP9McFwh95/9ufOGgBuZgSFurTf+QgKClJcXJz27Nnj9nN5AkENAAAA4EXOFtKUIqwBUKVqNJCCw8rvExxe0s/NCgsLtXPnzot2cmGCGgAAAMCbFORLNpukM4c0pU4Na5SXoz8WywUAlzIsFlm6DCi3j6VLfxkW18cM06ZN008//aT9+/frt99+05133qns7GwNHjzY5efyBsxRAwAAAHgRo+5lslxxjczdW2RccfUZQxpH/8YtZVHJnThGxz4yDH4XC8A9jEYtZbnqppLVn06eWDg4vCSkcdPS3IcOHdLdd9+t48ePKzIyUm3atNF7772n2rVru+V8nkZQAwAAAHgZo+5lUp3GFZ4c2GjcUmrUgsmEAbid0ailLA2aS4f2lEwcHBQq1WjgljtpSr344otuO7Y3IqgBAAAAvNC5hi6ENACqimGxSLUauXx1J5TgvkgAAAAAAAAvQVADAAAAAADgJbwmqJkzZ47i4+M1ZcoUR9vKlSs1atQodezYUfHx8dqyZUuFjvXmm2+qb9++SkhIUPfu3fX000+roKDAqc+CBQvUq1cvtWrVSkOGDNHPP//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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "before_dropoff_centers = before_drop.groupby('dropoff_cluster')\\\n", - " .apply(lambda cluster: get_centermost_point(list(zip(cluster['dropoff_lon'], cluster['dropoff_lat']))))\n", - "\n", - "before_dropoff_centers = pd.DataFrame(before_dropoff_centers.tolist(), columns=['center_lon', 'center_lat'])\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", + "plt.figure(figsize=(12, 8))\n", "\n", - "after_dropoff_centers = after_drop.groupby('dropoff_cluster')\\\n", - " .apply(lambda cluster: get_centermost_point(list(zip(cluster['dropoff_lon'], cluster['dropoff_lat']))))\n", + "# Plot pickup center points for before_pd\n", + "sns.scatterplot(x=\"center_lon\", y=\"center_lat\", data=before_dropoff_centers, hue='center_dropoff_cluster', s=100, alpha=0.7, palette='Reds', marker=\"x\", linewidth=2, label='Before')\n", "\n", - "after_dropoff_centers = pd.DataFrame(after_dropoff_centers.tolist(), columns=['center_lon', 'center_lat'])\n", + "# Plot pickup center points for after_pd\n", + "sns.scatterplot(x=\"center_lon\", y=\"center_lat\", data=after_dropoff_centers, hue='center_dropoff_cluster', s=100, alpha=0.7, palette='Greens', marker=\"o\", edgecolor='darkgreen',label='After')\n", "\n", + "# Set labels and legend\n", + "plt.xlabel('Longitude')\n", + "plt.ylabel('Latitude')\n", + "plt.title('Before & After - Dropoff Center Points')\n", + "plt.legend(bbox_to_anchor=(1, 1), loc='upper left')\n", "\n", - "print(before_dropoff_centers)\n", - "print(after_dropoff_centers)" + "# Show the plot\n", + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "id": "27dda7d4-b591-4f34-94e5-b4a1d77601fa", "metadata": {}, "outputs": [], "source": [ "from geopy.geocoders import GoogleV3\n", "\n", - "# Replace 'your_api_key' with your actual Google Maps API key\n", - "api_key = 'your_api_key'\n", + "api_key = 'AIzaSyB7J_Nrnu0upcNwzfYWJCLMdp75gHCt3vU'\n", "geolocator = GoogleV3(api_key=api_key)\n", "\n", "def get_address(lat, lon):\n", " location = geolocator.reverse((lat, lon), language='en')\n", - " return location.address\n", - "\n", - "# Example usage\n", - "latitude = 37.7749 # Replace with your latitude\n", - "longitude = -122.4194 # Replace with your longitude\n", + " return location.address\n" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "69877b90-e8f9-42a0-baf0-37352e92cc81", + "metadata": {}, + "outputs": [], + "source": [ + "# Getting addresses for all center points before and after program\n", "\n", - "address = get_address(latitude, longitude)\n", - "print(\"Address:\", address)\n" + "before_pickup_centers['center_address'] = before_pickup_centers.apply(lambda row: get_address(row['center_lat'], row['center_lon']), axis=1)\n", + "after_pickup_centers['center_address'] = after_pickup_centers.apply(lambda row: get_address(row['center_lat'], row['center_lon']), axis=1)\n", + "before_dropoff_centers['center_address'] = before_dropoff_centers.apply(lambda row: get_address(row['center_lat'], row['center_lon']), axis=1)\n", + "after_dropoff_centers['center_address'] = after_dropoff_centers.apply(lambda row: get_address(row['center_lat'], row['center_lon']), axis=1)\n" ] }, { "cell_type": "code", - "execution_count": 93, - "id": "ae766a41-28d2-4896-a8db-16a1967bb233", + "execution_count": 103, + "id": "a7a44770-79be-4f28-96ce-8ae13bd02d45", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "0 4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "1 5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "2 6134 S University Ave, Chicago, IL 60637, USA\n", + "3 1455 E 54th St, Chicago, IL 60615, USA\n", + "4 1208 E 47th St, Chicago, IL 60653, USA\n", + "5 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "6 QCM8+6W Chicago, IL, USA\n", + "7 4454 S Drexel Blvd, Chicago, IL 60653, USA\n", + "8 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", + "9 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", + "10 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", + "11 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "12 6124 S Ingleside Ave, Chicago, IL 60637, USA\n", + "13 5758 S Blackstone Ave, Chicago, IL 60637, USA\n", + "14 1120 E 54th St, Chicago, IL 60615, USA\n", + "Name: center_address, dtype: object\n", + "0 6134 S University Ave, Chicago, IL 60637, USA\n", + "1 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", + "2 1120 E 54th St, Chicago, IL 60615, USA\n", + "3 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "4 1455 E 54th St, Chicago, IL 60615, USA\n", + "5 5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "6 5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", + "7 4454 S Drexel Blvd, Chicago, IL 60653, USA\n", + "8 1208 E 47th St, Chicago, IL 60653, USA\n", + "9 4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "10 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", + "11 1322 E 54th St, Chicago, IL 60615, USA\n", + "12 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "13 5437 S E View Park, Chicago, IL 60615, USA\n", + "14 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", + "Name: center_address, dtype: object\n" + ] } ], "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", + "# Compare before and after center point locations for pickup\n", "\n", - "# Sample data\n", - "sns.set_style(\"whitegrid\")\n", - "plt.figure(figsize=(10, 8))\n", - "sns.scatterplot(x=\"pickup_lon\", y=\"pickup_lat\", data=pd_df[pd_df['pickup_lat'] != 0.0], hue='pickup_cluster', s=100)\n", - "plt.xlim( -87.6132781783, -87.5799482481)\n", - "plt.ylim(41.7706622278, 41.8132012947)\n", - "plt.xlabel('Pickup Longitude')\n", - "plt.ylabel('Pickup Latitude')\n", - "plt.title('Hexbin Plot of Pickup Clusters')\n", - "plt.show()\n", - "\n" + "print(before_pickup_centers['center_address'])\n", + "print(after_pickup_centers['center_address'])" ] }, { "cell_type": "code", - "execution_count": 83, - "id": "a6d73735-4cdb-46e0-88d5-fd7f6133b80a", + "execution_count": 104, + "id": "c56608d0-a429-4f11-a07a-c269172241ae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "41.8132012947\n" + "0 1455 E 54th St, Chicago, IL 60615, USA\n", + "1 5437 S E View Park, Chicago, IL 60615, USA\n", + "2 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", + "3 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "4 1208 E 47th St, Chicago, IL 60653, USA\n", + "5 5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "6 4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "7 6133 S Langley Ave, Chicago, IL 60637, USA\n", + "8 1120 E 54th St, Chicago, IL 60615, USA\n", + "9 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", + "10 QCM8+6W Chicago, IL, USA\n", + "11 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", + "12 5758 S Blackstone Ave, Chicago, IL 60637, USA\n", + "13 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "14 6134 S University Ave, Chicago, IL 60637, USA\n", + "Name: center_address, dtype: object\n", + "0 6124 S Ingleside Ave, Chicago, IL 60637, USA\n", + "1 1455 E 54th St, Chicago, IL 60615, USA\n", + "2 1208 E 47th St, Chicago, IL 60653, USA\n", + "3 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "4 5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "5 6134 S University Ave, Chicago, IL 60637, USA\n", + "6 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "7 QCGC+F2 Chicago, IL, USA\n", + "8 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", + "9 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", + "10 5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", + "11 5758 S Blackstone Ave, Chicago, IL 60637, USA\n", + "12 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", + "13 1120 E 54th St, Chicago, IL 60615, USA\n", + "14 1322 E 54th St, Chicago, IL 60615, USA\n", + "Name: center_address, dtype: object\n" ] - }, - { - "data": { - "text/plain": [ - "41.7706622278" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "print(before_pd['pickup_lat'].max())\n", - "before_pd['pickup_lat'].min()" + "# Compare before and after center point locations for dropoff\n", + "\n", + "print(before_dropoff_centers['center_address'])\n", + "print(after_dropoff_centers['center_address'])" ] }, { "cell_type": "code", - "execution_count": 84, - "id": "5fe5083a-133a-4f74-b1ca-2924d2746dc6", + "execution_count": 114, + "id": "99417bc1-36aa-425f-a0eb-642a4439e9a7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-87.5799482481\n" + "Average trip durations before program:\n" ] }, { - "data": { - "text/plain": [ - "-87.6132781783" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+---------------------+\n", + "|pickup_cluster|average_trip_duration|\n", + "+--------------+---------------------+\n", + "| 12| 5.889822766307327|\n", + "| 1| 2.488675938360686|\n", + "| 13| -0.2668360864040661|\n", + "| 6| 4.008822003113648|\n", + "| 3| 3.0738028416067356|\n", + "| 5| 5.083000499251123|\n", + "| 9| 5.054984036892515|\n", + "| 4| 4.514186295503212|\n", + "| 8| 1.1409299655568312|\n", + "| 7| 5.152899126290707|\n", + "| 10| 4.507743673194546|\n", + "| 11| 3.673683643803166|\n", + "| 14| 1.4098017088505561|\n", + "| 2| 5.398172323759791|\n", + "| 0| 3.5873064540855433|\n", + "+--------------+---------------------+\n", + "\n", + "Average trip durations after program:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 68583:=================================================> (44 + 3) / 47]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+---------------------+\n", + "|pickup_cluster|average_trip_duration|\n", + "+--------------+---------------------+\n", + "| 12| 5.933034317304683|\n", + "| 1| 5.011933510613307|\n", + "| 13| 5.364905177924569|\n", + "| 6| 5.379129328172225|\n", + "| 3| 5.23251788599123|\n", + "| 5| 4.838796923653058|\n", + "| 9| 5.577063550036523|\n", + "| 4| 4.933941452048683|\n", + "| 8| 5.47335072679836|\n", + "| 7| 7.1022727272727275|\n", + "| 10| 4.648405828859428|\n", + "| 11| 4.880330722367276|\n", + "| 14| 4.936453228499111|\n", + "| 2| 4.366875084341685|\n", + "| 0| 5.608800643949557|\n", + "+--------------+---------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] } ], "source": [ - "print(before_pd['pickup_lon'].max())\n", - "before_pd['pickup_lon'].min()" + "# Find ride trip duration changes before and after program based on pickup clusters\n", + "\n", + "before_pickup_avg_time = before.groupBy('pickup_cluster').agg(F.avg('trip_duration').alias('average_trip_duration'))\n", + "after_pickup_avg_time = after.groupBy('pickup_cluster').agg(F.avg('trip_duration').alias('average_trip_duration'))\n", + "\n", + "print('Average trip durations before program:')\n", + "before_pickup_avg_time.show()\n", + "\n", + "print('Average trip durations after program:')\n", + "after_pickup_avg_time.show()" ] }, { "cell_type": "code", - "execution_count": 88, - "id": "a9389626-feea-405d-b9c7-1836597b11e9", + "execution_count": 116, + "id": "f9d5abcc-5578-4ccb-ae23-f8d6a4be881b", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Average trip durations before program:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------+\n", + "|pickup_cluster|average_trips|\n", + "+--------------+-------------+\n", + "| 12| 5473|\n", + "| 1| 43933|\n", + "| 13| 4722|\n", + "| 6| 11562|\n", + "| 3| 22804|\n", + "| 5| 16024|\n", + "| 9| 8457|\n", + "| 4| 11208|\n", + "| 8| 10452|\n", + "| 7| 2518|\n", + "| 10| 71284|\n", + "| 11| 18574|\n", + "| 14| 6203|\n", + "| 2| 6894|\n", + "| 0| 6523|\n", + "+--------------+-------------+\n", + "\n", + "Average trip durations after program:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 68611:===================================================> (46 + 1) / 47]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------+\n", + "|pickup_cluster|average_trips|\n", + "+--------------+-------------+\n", + "| 12| 38348|\n", + "| 1| 106423|\n", + "| 13| 9386|\n", + "| 6| 18859|\n", + "| 3| 17332|\n", + "| 5| 144197|\n", + "| 9| 9583|\n", + "| 4| 40343|\n", + "| 8| 10732|\n", + "| 7| 528|\n", + "| 10| 45635|\n", + "| 11| 41364|\n", + "| 14| 18569|\n", + "| 2| 22231|\n", + "| 0| 37270|\n", + "+--------------+-------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] } ], "source": [ - "import geopandas as gpd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Assuming pd_df is your original DataFrame\n", + "# Find average trips before and after program based on pickup clusters\n", "\n", - "# Set latitude and longitude limits\n", - "min_lat, max_lat = 41.7706622278, 41.8132012947\n", - "min_lon, max_lon = -87.6132781783, -87.5799482481\n", + "before_avg_trips = before.groupBy('pickup_cluster').agg(F.count('ID').alias('average_trips'))\n", + "after_avg_trips = after.groupBy('pickup_cluster').agg(F.count('ID').alias('average_trips'))\n", "\n", - "# Generate more granular points within the specified range\n", - "num_points = 1000\n", - "granular_lat = np.linspace(min_lat, max_lat, num_points)\n", - "granular_lon = np.linspace(min_lon, max_lon, num_points)\n", + "print('Average trip durations before program:')\n", + "before_avg_trips.show()\n", "\n", - "# Create a DataFrame with granular data points\n", - "granular_df = pd.DataFrame({'pickup_lat': granular_lat, 'pickup_lon': granular_lon})\n", - "\n", - "# Plot the granular scatter plot\n", - "sns.set_style(\"whitegrid\")\n", - "plt.figure(figsize=(12, 8))\n", - "plot = sns.scatterplot(x=\"pickup_lat\", y=\"pickup_lon\", data=granular_df, s=10, color='blue')\n", - "\n", - "# Set x-axis and y-axis labels\n", - "plt.xlabel(\"Latitude\")\n", - "plt.ylabel(\"Longitude\")\n", - "\n", - "plt.show()\n" + "print('Average trip durations after program:')\n", + "after_avg_trips.show()" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "99417bc1-36aa-425f-a0eb-642a4439e9a7", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f9d5abcc-5578-4ccb-ae23-f8d6a4be881b", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null,